The role of metabolites and metabolomics in clinically applicable biomarkers of disease

Metabolomic Analysis Reveals the Adaptation in the P. przewalskii to Se-Deprived Environment

The Procapra przewalskii inhabits in a selenium (Se)-deprived environment in long-term, but they have no pathological manifestations due to the Se deprivation. This study aimed to reveal the underlying adaptation induced by Se deprivation. In the analysis, a total of 93 significantly changed metabolites were identified in positive and negative ion modes, including 46 upregulated and 47 downregulated compounds in the Se-deprived group. The differential metabolites were annotated as the major molecules in bile acid biosynthesis, biosynthesis of unsaturated fatty acids, and pyrimidine metabolism, respectively. This study systematically analyzed the serum metabolomics characteristics of P. przewalskii under Se-deprived conditions for the first time, providing a basis for further understanding of the metabolic mechanism of P. przewalskii in the Se-deprived environment.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753-0.9825), 0.7 and 1, respectively.

A Feature Selection Approach Guided an Early Prediction of Anthocyanin Accumulation Using Massive Untargeted Metabolomics Data in Mulberry

Identifying the early predictive biomarkers or compounds represents a pivotal task for guiding a targeted agricultural practice. Despite the various available tools, it remains challenging to define the ideal compound combination and thereby elaborate an effective predictive model fitting that. Hence, we employed a stepwise feature selection approach followed by a maximum relevance and minimum redundancy (MRMR) on the untargeted metabolism in four mulberry genotypes at different fruit developmental stages (FDSs). Thus, we revealed that 7 out of 226 differentially abundant metabolites (DAMs) explained up to 80% variance of anthocyanin based on linear regression model and stepwise feature selection approach accompanied by an MRMR across the genotypes over the FDSs. Among them, the phosphoenolpyruvate, d-mannose and shikimate show the top 3 attribution indexes to the accumulation of anthocyanin in the fruits of these genotypes across the four FDSs. The obtained results were further validated by assessing the regulatory genes expression levels and the targeted metabolism approach. Taken together, our findings provide valuable evidences on the fact that the anthocyanin biosynthesis is somehow involved in the coordination between the carbon metabolism and secondary metabolic pathway. Our report highlights as well the importance of using the feature selection approach for the predictive biomarker identification issued from the untargeted metabolomics data.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer's Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ1-42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Amino acids in inflammatory bowel diseases: Modern diagnostic tools and methodologies

Amino acids are crucial building blocks of living organisms. Together with their derivatives, they participate in many intracellular processes to act as hormones, neuromodulators, and neurotransmitters. For several decades amino acids have been studied for their potential as markers of various diseases, including inflammatory bowel diseases. Subsequent improvements in sample pretreatment, separation, and detection methods have enabled the specific and very sensitive determination of these molecules in multicomponent matrices-biological fluids and tissues. The information obtained from targeted amino acid analysis (biomarker-based analytical strategy) can be further used for early diagnostics, to monitor the course of the disease or compliance of the patients. This review will provide an insight into current knowledge about inflammatory bowel diseases, the role of proteinogenic amino acids in intestinal inflammation and modern analytical techniques used in its diagnosis and disease activity monitoring. Current advances in the analysis of amino acids focused on sample pretreatment, separation strategy, or detection methods are highlighted, and their potential in clinical laboratories is discussed. In addition, the latest clinical data obtained from the metabolomic profiling of patients suffering from inflammatory bowel diseases are summarized with a focus on proteinogenic amino acids.

Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

Metabolic Biomarkers in B-Cell Lymphomas for Early Diagnosis and Prediction, as Well as Their Influence on Prognosis and Treatment

B-cell lymphomas exhibit a vast variety of clinical and histological characteristics that might complicate the diagnosis. Timely diagnosis is crucial, as treatments for aggressive subtypes are considered successful and frequently curative, whereas indolent B-cell lymphomas are incurable and often need several therapies. The purpose of this review is to explore the current advancements achieved in B-cell lymphomas metabolism and how these indicators help to early detect metabolic changes in B-cell lymphomas and the use of predictive biological markers in refractory or relapsed disease. Since the year 1920, the Warburg effect has been known as an integral part of metabolic reprogramming. Compared to normal cells, cancerous cells require more glucose. These cancer cells undergo aerobic glycolysis instead of oxidative phosphorylation to metabolize glucose and form lactate as an end product. With the help of these metabolic alterations, a novel biomass is generated by the formation of various precursors. An aggressive metabolic phenotype is an aerobic glycolysis that has the advantage of producing high-rate ATP and preparing the biomass for the amino acid, as well as fatty acid, synthesis needed for a rapid proliferation of cells, while aerobic glycolysis is commonly thought to be the dominant metabolism in cancer cells. Later on, many metabolic biomarkers, such as increased levels of lactate dehydrogenase (LDH), plasma lactate, and deficiency of thiamine in B-cell lymphoma patients, were discovered. Various kinds of molecules can be used as biomarkers, such as genes, proteins, or hormones, because they all refer to body health. Here, we focus only on significant metabolic biomarkers in B-cell lymphomas. In conclusion, many metabolic biomarkers have been shown to have clinical validity, but many others have not been subjected to extensive testing to demonstrate their clinical usefulness in B-cell lymphoma. Furthermore, they play an essential role in the discovery of new therapeutic targets.

Discovery and validation of acetyl-L-carnitine in serum for diagnosis of major depressive disorder and remission status through metabolomic approach

Major depressive disorder (MDD) is one of the most common psychiatric disorders that accompany psychophysiological and mood changes. However, the pathophysiology-based disease mechanism of MDD is not yet fully understood, and diagnosis is also conducted through interviews with clinicians and patients. Diagnosis and treatment of MDD are limited due to the absence of biomarkers underlying the pathophysiological mechanisms of MDD. Although various attempts have been made to discover metabolite biomarkers for the diagnosis and treatment response of MDD, problems with sample size and consistency of results have limited clinical application. In addition, it was reported that future biomarker studies must consider exposure to antidepressants, which is the main cause of heterogeneity in depression subgroups. Therefore, the purpose of this study is to discover and validate biomarkers for the diagnosis of depression in consideration of exposure to drug treatment including antidepressants that contribute to the heterogeneity of the MDD subgroup. In the biomarker discovery and validation set, the disease group consisted of a mixture of patients exposed and unexposed to drug treatment including antidepressants for the treatment of MDD. The serum metabolites that differed between the MDD patients and the control group were profiled using mass spectrometry. The validation set including the remission group was used to verify the effectiveness as a biomarker for the diagnosis of depression and determination of remission status. The presence of different metabolites between the two groups was confirmed through serum metabolite profiling between the MDD patient group and the control group. Finally, Acetylcarnitine was selected as a biomarker. In validation, acetylcarnitine was significantly decreased in MDD and was distinguished from remission status. This study confirmed that the discovered acetylcarnitine has potential as a biomarker for diagnosing depression and determining remission status, regardless of exposure to drug treatment including antidepressants.

Application of Metabolomics to Identify Potential Biomarkers for the Early Diagnosis of Coronary Heart Disease

Coronary heart disease (CHD) is one of the leading causes of deaths globally. Identification of serum metabolic biomarkers for its early diagnosis is thus much desirable. Serum samples were collected from healthy controls (n = 86) and patients with CHD (n = 166) and subjected to untargeted and targeted metabolomics analyses. Subsequently, potential biomarkers were detected and screened, and a clinical model was developed for diagnosing CHD. Four dysregulated metabolites, namely PC(17:0/0:0), oxyneurine, acetylcarnitine, and isoundecylic acid, were identified. Isoundecylic acid was not found in Human Metabolome Database, so we could not validate differences in its relative abundance levels. Further, the clinical model combining serum oxyneurine, triglyceride, and weight was found to be more robust than that based on PC(17:0/0:0), oxyneurine, and acetylcarnitine (AUC = 0.731 vs. 0.579, sensitivity = 83.0 vs. 75.5%, and specificity = 64.0 vs. 46.5%). Our findings indicated that serum metabolomics is an effective method to identify differential metabolites and that serum oxyneurine, triglyceride, and weight appear to be promising biomarkers for the early diagnosis of CHD.

Metabolomics analysis for diagnosis and biomarker discovery of transthyretin amyloidosis

Untargeted metabolomics is a well-established technique and a powerful tool to find potential plasma biomarkers for early diagnosing hereditary transthyretin amyloidosis. Hereditary transthyretin amyloidosis (ATTRv) is a disabling and fatal disease with different clinical features such as polyneuropathy, cardiomyopathy, different gastrointestinal symptoms and renal failure. Plasma specimens collected from 27 patients with ATTRv (ATTRV30M), 26 asymptomatic TTRV30M carriers and 26 control individuals were subjected to gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS)-based metabolomics analysis. Partial least squares discriminant and univariate analysis was used to analyse the data. The models constructed by Partial least squares-discriminant analysis (PLS-DA) could clearly discriminate ATTRV30M patients from controls and asymptomatic TTRV30M carriers. In total, 24 plasma metabolites (VIP > 1.0 and p < .05) were significantly altered in ATTRV30M patient group (6 increased and 18 decreased). Eleven of these distinguished the ATTRV30M group from both controls and TTRV30M carriers. Plasma metabolomics analysis revealed marked changes in several pathways in patients with ATTRV30M amyloidosis. Statistical analysis identified a panel of biomarkers that could effectively separate controls/TTRV30M carriers from ATTRV30M patients. These biomarkers can potentially be used to diagnose patients at an early stage of the disease.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

NMR-Based Metabolomics Revealed the Underlying Inflammatory Pathology in Reactive Arthritis Synovial Joints

Reactive arthritis (ReA) is an aseptic synovitis condition that often develops 2-4 weeks after a distant (extra-articular) infection with Chlamydia, Salmonella, Shigella, Campylobacter, and Yersinia species. The metabolic changes in the synovial fluid (SF) may serve as indicative markers to both improve the diagnostic accuracy and understand the underlying inflammatory pathology of ReA. With this aim, the metabolic profiles of SF collected from ReA (n = 58) and non-ReA, i.e., rheumatoid arthritis (RA, n = 21) and osteoarthritis (OA, n = 20) patients, respectively, were measured using NMR spectroscopy and compared using orthogonal partial least-squares discriminant analysis (OPLS-DA). The discriminatory metabolic features were further evaluated for their diagnostic potential using the receiver operating characteristic (ROC) curve analysis. Compared to RA, two (alanine and carnitine), and compared to OA, six (NAG, glutamate, glycerol, isoleucine, alanine, and glucose) metabolic features were identified as diagnostic biomarkers. We further demonstrated the impact of ReA synovitis condition on the serum metabolic profiles through performing a correlation analysis. The Pearson rank coefficient (r) was estimated for 38 metabolites (profiled in both SF and serum samples obtained in pair from ReA patients) and was found significantly positive for 71% of the metabolites (r ranging from 0.17 to 0.87).

Metabolic Dysfunction Biomarkers as Predictors of Early Diabetes

During the pathophysiological course of type 2 diabetes (T2D), several metabolic imbalances occur. There is increasing evidence that metabolic dysfunction far precedes clinical manifestations. Thus, knowing and understanding metabolic imbalances is crucial to unraveling new strategies and molecules (biomarkers) for the early-stage prediction of the disease's non-clinical phase. Lifestyle interventions must be made with considerable involvement of clinicians, and it should be considered that not all patients will respond in the same manner. Individuals with a high risk of diabetic progression will present compensatory metabolic mechanisms, translated into metabolic biomarkers that will therefore show potential predictive value to differentiate between progressors/non-progressors in T2D. Specific novel biomarkers are being proposed to entrap prediabetes and target progressors to achieve better outcomes. This study provides a review of the latest relevant biomarkers in prediabetes. A search for articles published between 2011 and 2021 was conducted; duplicates were removed, and inclusion criteria were applied. From the 29 studies considered, a survey of the most cited (relevant) biomarkers was conducted and further discussed in the two main identified fields: metabolomics, and miRNA studies.

Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

Metabolomics-based technologies map in vivo biochemical changes that may be used as early indicators of pathological abnormalities prior to the development of clinical symptoms in neurological conditions. Metabolomics may also reveal biochemical pathways implicated in tissue dysfunction and damage and thus assist in the development of novel targeted therapeutics for neuroinflammation and neurodegeneration. Metabolomics holds promise as a non-invasive, high-throughput and cost-effective tool for early diagnosis, follow-up and monitoring of treatment response in multiple sclerosis (MS), in combination with clinical and imaging measures. In this review, we offer evidence in support of the potential of metabolomics as a biomarker and drug discovery tool in MS. We also use pathway analysis of metabolites that are described as potential biomarkers in the literature of MS biofluids to identify the most promising molecules and upstream regulators, and show novel, still unexplored metabolic pathways, whose investigation may open novel avenues of research.

Organic Bioelectronic Devices for Metabolite Sensing

Electrochemical detection of metabolites is essential for early diagnosis and continuous monitoring of a variety of health conditions. This review focuses on organic electronic material-based metabolite sensors and highlights their potential to tackle critical challenges associated with metabolite detection. We provide an overview of the distinct classes of organic electronic materials and biorecognition units used in metabolite sensors, explain the different detection strategies developed to date, and identify the advantages and drawbacks of each technology. We then benchmark state-of-the-art organic electronic metabolite sensors by categorizing them based on their application area (in vitro, body-interfaced, in vivo, and cell-interfaced). Finally, we share our perspective on using organic bioelectronic materials for metabolite sensing and address the current challenges for the devices and progress to come.

Multiplatform metabolomic analysis of the R6/2 mouse model of Huntington's disease

Huntington's disease (HD) is a progressive, neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. To investigate the metabolic alterations that occur in HD, here we examined plasma and whole-brain metabolomic profiles of the R6/2 mouse model of HD. Plasma and brain metabolomic analyses were conducted using capillary electrophoresis-mass spectrometry (CE-MS). In addition, liquid chromatography-mass spectrometry (LC-MS) was also applied to plasma metabolomic analyses, to cover the broad range of metabolites with various physical and chemical properties. Various metabolic alterations were identified in R6/2 mice. We report for the first time the perturbation of histidine metabolism in the brain of R6/2 mice, which was signaled by decreases in neuroprotective dipeptides and histamine metabolites, indicative of neurodegeneration and an altered histaminergic system. Other differential metabolites were related to arginine metabolism and cysteine and methionine metabolism, suggesting upregulation of the urea cycle, perturbation of energy homeostasis, and an increase in oxidative stress. In addition, remarkable changes in specific lipid classes are indicative of dysregulation of lipid metabolism. These findings provide a deeper insight into the metabolic alterations that occur in HD and provide a foundation for the future development of HD therapeutics.

Cerebrospinal fluid metabolomics: detection of neuroinflammation in human central nervous system disease

The high morbidity and mortality of neuroinflammatory diseases drives significant interest in understanding the underlying mechanisms involved in the innate and adaptive immune response of the central nervous system (CNS). Diagnostic biomarkers are important to define treatable neuroinflammation. Metabolomics is a rapidly evolving research area offering novel insights into metabolic pathways, and elucidation of reliable metabolites as biomarkers for diseases. This review focuses on the emerging literature regarding the detection of neuroinflammation using cerebrospinal fluid (CSF) metabolomics in human cohort studies. Studies of classic neuroinflammatory disorders such as encephalitis, CNS infection and multiple sclerosis confirm the utility of CSF metabolomics. Additionally, studies in neurodegeneration and neuropsychiatry support the emerging potential of CSF metabolomics to detect neuroinflammation in common CNS diseases such as Alzheimer's disease and depression. We demonstrate metabolites in the tryptophan-kynurenine pathway, nitric oxide pathway, neopterin and major lipid species show moderately consistent ability to differentiate patients with neuroinflammation from controls. Integration of CSF metabolomics into clinical practice is warranted to improve recognition and treatment of neuroinflammation.

Biomarkers for the Clinical Diagnosis of Alzheimer's Disease: Metabolomics Analysis of Brain Tissue and Blood

With an increase in aging populations worldwide, age-related diseases such as Alzheimer's disease (AD) have become a global concern. At present, a cure for neurodegenerative disease is lacking. There is an urgent need for a biomarker that can facilitate the diagnosis, classification, prognosis, and treatment response of AD. The recent emergence of highly sensitive mass-spectrometry platforms and high-throughput technology can be employed to discover and catalog vast datasets of small metabolites, which respond to changed status in the body. Metabolomics analysis provides hope for a better understanding of AD as well as the subsequent identification and analysis of metabolites. Here, we review the state-of-the-art emerging candidate biomarkers for AD.

Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease

Bicuspid aortic valve (BAV) is the most common congenital heart defect responsible for valvular and aortic complications in affected patients. Causes and mechanisms of this pathology are still elusive and thus the lack of early detection biomarkers leads to challenges in its diagnosis and prevention of associated cardiovascular anomalies. The aim of this study was to explore the potential use of urine Nuclear Magnetic Resonance (NMR) metabolomics to evaluate a molecular fingerprint of BAV. Both multivariate and univariate statistical analyses were performed to compare the urinary metabolome of 20 patients with BAV with that of 24 matched controls. Orthogonal partial least squared discriminant analysis (OPLS-DA) showed statistically significant discrimination between cases and controls, suggesting seven metabolites (3-hydroxybutyrate, alanine, betaine, creatine, glycine, hippurate, and taurine) as potential biomarkers. Among these, glycine, hippurate and taurine individually displayed medium sensitivity and specificity by receiver operating characteristic (ROC) analysis. Pathway analysis indicated two metabolic pathways likely perturbed in BAV subjects. Possible contributions of gut microbiota activity and energy imbalance are also discussed. These results constitute encouraging preliminary findings in favor of the use of urine-based metabolomics for early diagnosis of BAV.

Assessment of the amino acid profile in Thai patients with type 2 diabetes mellitus using liquid chromatography-mass spectrometry

Background

Type 2 diabetes mellitus (T2DM) is a global health problem. Early identification of those at risk is necessary to prevent its onset through lifestyle and pharmacologic interventions. T2DM is characterized by metabolic abnormalities, including protein metabolism. Evaluation of the amino acid profile might be beneficial for early assessment.

Methods

Liquid chromatography-mass spectrometry was performed to separate and quantify plasma amino acids from two groups of Thai individuals, patients with T2DM (n=103) and healthy individuals (n=104). Multivariate analysis was applied to compare free amino acid levels between groups. Subgroup analyses of patients with T2DM were performed to assess the association between amino acid profiles and important T2DM clinical characteristics.

Results

The multivariate analysis showed that glutamic acid was significantly associated with T2DM (OR 1.113, 95% CI 1.006 to 1.231) and results from the subgroup analyses showed that this correlation was significant in all subgroups of patients (p<0.05).

Conclusions

This finding needs to be confirmed in larger groups of patients with T2DM to explore glutamic acid as a biomarker for early prevention in particular at-risk groups. An in-depth understanding of the involvement of glutamic acid in T2DM could enhance our understanding of the disease and potentially provide novel interventions.

Metabolomic Characterization of Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS)

Introduction

PANS is a controversial clinical entity, consisting of a complex constellation of psychiatric symptoms, adventitious changes, and expression of various serological alterations, likely sustained by an autoimmune/inflammatory disease. Detection of novel biomarkers of PANS is highly desirable for both diagnostic and therapeutic management of affected patients. Analysis of metabolites has proven useful in detecting biomarkers for other neuroimmune-psychiatric diseases. Here, we utilize the metabolomics approach to determine whether it is possible to define a specific metabolic pattern in patients affected by PANS compared to healthy subjects.

Design

This observational case-control study tested consecutive patients referred for PANS between June 2019 to May 2020. A PANS diagnosis was confirmed according to the PANS working criteria (National Institute of Mental Health [NIMH], 2010). Healthy age and sex-matched subjects were recruited as controls.

Methods

Thirty-four outpatients referred for PANS (mean age 9.5 years; SD 2.9, 71% male) and 25 neurotypical subjects matched for age and gender, were subjected to metabolite analysis. Serum samples were obtained from each participant and were analyzed using Nuclear Magnetic Resonance (NMR) spectroscopy. Subsequently, multivariate and univariate statistical analyses and Receiver Operator Curves (ROC) were performed.

Results

Separation of the samples, in line with the presence of PANS diagnosis, was observed by applying a supervised model (R²X = 0.44, R²Y = 0.54, Q² = 0.44, p-value < 0.0001). The significantly altered variables were 2-Hydroxybutyrate, glycine, glutamine, histidine, tryptophan. Pathway analysis indicated that phenylalanine, tyrosine, and tryptophan metabolism, as well as glutamine and glutamate metabolism, exhibited the largest deviations from neurotypical controls.

Conclusion

We found a unique plasma metabolic profile in PANS patients, significantly differing from that of healthy children, that suggests the involvement of specific patterns of neurotransmission (tryptophan, glycine, histamine/histidine) as well as a more general state of neuroinflammation and oxidative stress (glutamine, 2-Hydroxybutyrate, and tryptophan-kynurenine pathway) in the disorder. This metabolomics study offers new insights into biological mechanisms underpinning the disorder and supports research of other potential biomarkers implicated in PANS.

Heat Shock Factor 1 as a Prognostic and Diagnostic Biomarker of Gastric Cancer

Identification of effective prognostic and diagnostic biomarkers is needed to improve the diagnosis and treatment of gastric cancer. Early detection of gastric cancer through diagnostic markers can help establish effective treatments. Heat shock factor 1 (HSF1), presented in this review, is known to be regulated by a broad range of transcription factors, including those characterized in various malignant tumors, including gastric cancer. Particularly, it has been demonstrated that HSF1 regulation in various cancers is correlated with different processes, such as cell death, proliferation, and metastasis. Due to the effect of HSF1 on the initiation, development, and progression of various tumors, it is considered as an important gene for understanding and treating tumors. Additionally, HSF1 exhibits high expression in various cancers, and its high expression adversely affects the prognosis of various cancer patients, thereby suggesting that it can be used as a novel, predictive, prognostic, and diagnostic biomarker for gastric cancer. In this review, we discuss the literature accumulated in recent years, which suggests that there is a correlation between the expression of HSF1 and prognosis of gastric cancer patients through public data. Consequently, this evidence also indicates that HSF1 can be established as a powerful biomarker for the prognosis and diagnosis of gastric cancer.

Few-Layer MoS2 Photodetector Arrays for Ultrasensitive On-Chip Enzymatic Colorimetric Analysis

Enzymatic colorimetric analysis of metabolites provides signatures of energy conversion and biosynthesis associated with disease onsets and progressions. Miniaturized photodetectors based on emerging two-dimensional transition metal dichalcogenides (TMDCs) promise to advance point-of-care diagnosis employing highly sensitive enzymatic colorimetric detection. Reducing diagnosis costs requires a batched multisample assay. The construction of few-layer TMDC photodetector arrays with consistent performance is imperative to realize optical signal detection for a miniature batched multisample enzymatic colorimetric assay. However, few studies have promoted an optical reader with TMDC photodetector arrays for on-chip operation. Here, we constructed 4 × 4 pixel arrays of miniaturized molybdenum disulfide (MoS₂) photodetectors and integrated them with microfluidic enzyme reaction chambers to create an optoelectronic biosensor chip device. The fabricated device allowed us to achieve arrayed on-chip enzymatic colorimetric detection of d-lactate, a blood biomarker signifying the bacterial translocation from the intestine, with a limit of detection that is 1000-fold smaller than the clinical baseline, a 10 min assay time, high selectivity, and reasonably small variability across the entire arrays. The enzyme (Ez)/MoS₂ optoelectronic biosensor unit consistently detected d-lactate in clinically important biofluids, such as saliva, urine, plasma, and serum of swine and humans with a wide detection range (10^-3-10³ μg/mL). Furthermore, the biosensor enabled us to show that high serum d-lactate levels are associated with the symptoms of systemic infection and inflammation. The lensless, optical waveguide-free device architecture should readily facilitate development of a monolithically integrated hand-held module for timely, cost-effective diagnosis of metabolic disorders in near-patient settings.

Growing Human Hepatocellular Tumors Undergo a Global Metabolic Reprogramming

Hepatocellular carcinoma (HCC) is a common malignancy with poor prognosis, high morbidity and mortality concerning with lack of effective diagnosis and high postoperative recurrence. Similar with other cancers, HCC cancer cells have to alter their metabolism to adapt to the changing requirements imposed by the environment of the growing tumor. In less vascularized regions of tumor, cancer cells experience hypoxia and nutrient starvation. Here, we show that HCC undergoes a global metabolic reprogramming during tumor growth. A combined proteomics and metabolomics analysis of paired peritumoral and tumor tissues from 200 HCC patients revealed liver-specific metabolic reprogramming and metabolic alterations with increasing tumor sizes. Several proteins and metabolites associated with glycolysis, the tricarboxylic acid cycle and pyrimidine synthesis were found to be differentially regulated in serum, tumor and peritumoral tissue with increased tumor sizes. Several prognostic metabolite biomarkers involved in HCC metabolic reprogramming were identified and integrated with clinical and pathological data. We built and validated this combined model to discriminate against patients with different recurrence risks. An integrated and comprehensive metabolomic analysis of HCC is provided by our present work. Metabolomic alterations associated with the advanced stage of the disease and poor clinical outcomes, were revealed. Targeting cancer metabolism may deliver effective therapies for HCC.

Microbial Metabolomics: From Methods to Translational Applications

Most microbe-associated infectious diseases severely affect human health. However, clinical diagnosis of pathogenic diseases remains challenging due to the lack of specific and highly reliable methods. To better understand the diagnosis, pathogenesis, and treatment of these diseases, systems biology-driven metabolomics goes beyond the annotated phenotype and better targets the functions than conventional approaches. As a novel strategy for analysis of metabolomes in microbes, microbial metabolomics has been recently used to study many diseases, such as obesity, urinary tract infection (UTI), and hepatitis C. In this chapter, we attempt to introduce various microbial metabolomics methods to better interpret the microbial metabolism underlying a diversity of infectious diseases and inspire scientists to pay more attention to microbial metabolomics, enabling broadly and efficiently its translational applications to infectious diseases, from molecular diagnosis to therapeutic discovery.

Metabolomics shows the Australian dingo has a unique plasma profile

Dingoes occupy a wide range of the Australian mainland and play a crucial role as an apex predator with a generalist omnivorous feeding behaviour. Dingoes are ecologically, phenotypically and behaviourally distinct from modern breed dogs and have not undergone artificial selection since their arrival in Australia. In contrast, humans have selected breed dogs for novel and desirable traits. First, we examine whether the distinct evolutionary histories of dingoes and domestic dogs has lead to differences in plasma metabolomes. We study metabolite composition differences between dingoes (n = 15) and two domestic dog breeds (Basenji n = 9 and German Shepherd Dog (GSD) n = 10). Liquid chromatography mass spectrometry, type II and type III ANOVA with post-hoc tests and adjustments for multiple comparisons were used for data evaluation. After accounting for within group variation, 62 significant metabolite differences were detected between dingoes and domestic dogs, with the majority of differences in protein (n = 14) and lipid metabolites (n = 12), mostly lower in dingoes. Most differences were observed between dingoes and domestic dogs and fewest between the domestic dog breeds. Next, we collect a second set of data to investigate variation between pure dingoes (n = 10) and dingo-dog hybrids (n = 10) as hybridisation is common in regional Australia. We detected no significant metabolite differences between dingoes and dingo-dog hybrids after Bonferroni correction. However, power analysis showed that increasing the sample size to 15 could result in differences in uridine 5′-diphosphogalactose (UDPgal) levels related to galactose metabolism. We suggest this may be linked to an increase in Amylase 2B copy number in hybrids. Our study illustrates that the dingo metabolome is significantly different from domestic dog breeds and hybridisation is likely to influence carbohydrate metabolism.

12-Plex UHPLC-MS/MS analysis of sarcosine in human urine using integrated principle of multiplex tags chemical isotope labeling and selective imprint enriching

Urinary sarcosine was considered to be a potential biomarker for prostate cancer (Pca). In this work, an integrated strategy of multiplex tags chemical isotope labeling (MTCIL) combined with magnetic dispersive solid phase extraction (MDSPE), was proposed for specific extraction and high-throughput determination of sarcosine by ultra high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). In the past three months, we have developed 8-plex MTCIL reagents with excellent qualitative and quantitative performance. In this work, the multiplexing capacity of MTCIL reagents (MTCIL360/361/362/363/364/365/366/375/376/378/379/381) was increased 1.5-fold from 8-plex to 12-plex. MTCIL359 was prepared and used to label sarcosine standard as internal standard (IS). The structural analogue derivative (MTCIL373-sarcosine) of all targeted MTCIL-sarcosine derivatives was synthesized and used as a novel dummy template to prepare dummy magnetic molecularly imprinted polymers (DMMIPs). The integration of MTCIL and DMMIPs procedures were extremely favorable to excellent chromatographic separation and efficient mass spectrometric detection. The labeling efficiency, chromatographic retention and mass spectrometry responses of MTCIL reagents were consistent for sarcosine. In a single UHPLC-MS/MS run (2.0 min), this method can simultaneously quantify sarcosine in 12-plex urine samples and achieve unbiased concentrations comparison between different urine samples. Analytical parameters including linearity (R² 0.989-0.997), detection limits (0.02 nM), precision (2.6-11.5%), accuracy (96.1-107.4%), matrix effect, labeling and extraction efficiency were carefully validated. The proposed method was successfully applied for urinary sarcosine determination of healthy male individuals and Pca patients. It was found that the sarcosine concentrations in these two groups were statistically extremely significantly different (P < 0.001). The developed method was a powerful analytical tool to substantially promote the analysis throughput and large-scale experiments about the potential biomarker research.

Driving success in personalized medicine through AI-enabled computational modeling

The development of successful drugs is expensive and time-consuming because of high clinical attrition rates. This is caused partially by the rupture seen in the translatability of the drug from the bench to the clinic in the context of personalized medicine. Artificial intelligence (AI)-driven platforms integrated with mechanistic modeling have become instrumental in accelerating the drug development process by leveraging data ubiquitously across the various phases. AI can counter the deficiencies and ambiguities that arise during the classical drug development process while reducing human intervention and bridging the translational gap in discovering the connections between drugs and diseases.

Charged metabolite biomarkers of food intake assessed via plasma metabolomics in a population-based observational study in Japan

Food intake biomarkers can be critical tools that can be used to objectively assess dietary exposure for both epidemiological and clinical nutrition studies. While an accurate estimation of food intake is essential to unravel associations between the intake and specific health conditions, random and systematic errors affect self-reported assessments. This study aimed to clarify how habitual food intake influences the circulating plasma metabolome in a free-living Japanese regional population and to identify potential food intake biomarkers. To achieve this aim, we conducted a cross-sectional analysis as part of a large cohort study. From a baseline survey of the Tsuruoka Metabolome Cohort Study, 7,012 eligible male and female participants aged 40-69 years were chosen for this study. All data on patients' health status and dietary intake were assessed via a food frequency questionnaire, and plasma samples were obtained during an annual physical examination. Ninety-four charged plasma metabolites were measured using capillary electrophoresis mass spectrometry, by a non-targeted approach. Statistical analysis was performed using partial-least-square regression. A total of 21 plasma metabolites were likely to be associated with long-term food intake of nine food groups. In particular, the influential compounds in each food group were hydroxyproline for meat, trimethylamine-N-oxide for fish, choline for eggs, galactarate for dairy, cystine and betaine for soy products, threonate and galactarate for carotenoid-rich vegetables, proline betaine for fruits, quinate and trigonelline for coffee, and pipecolate for alcohol, and these were considered as prominent food intake markers in Japanese eating habits. A set of circulating plasma metabolites was identified as potential food intake biomarkers in the Japanese community-dwelling population. These results will open the way for the application of new reliable dietary assessment tools not by self-reported measurements but through objective quantification of biofluids.

Tissue-Specific Landscape of Metabolic Dysregulation during Ageing

The dysregulation of cellular metabolism is a hallmark of ageing. To understand the metabolic changes that occur as a consequence of the ageing process and to find biomarkers for age-related diseases, we conducted metabolomic analyses of the brain, heart, kidney, liver, lung and spleen in young (9-10 weeks) and old (96-104 weeks) wild-type mice [mixed genetic background of 129/J and C57BL/6] using NMR spectroscopy. We found differences in the metabolic fingerprints of all tissues and distinguished several metabolites to be altered in most tissues, suggesting that they may be universal biomarkers of ageing. In addition, we found distinct tissue-clustered sets of metabolites throughout the organism. The associated metabolic changes may reveal novel therapeutic targets for the treatment of ageing and age-related diseases. Moreover, the identified metabolite biomarkers could provide a sensitive molecular read-out to determine the age of biologic tissues and organs and to validate the effectiveness and potential off-target effects of senolytic drug candidates on both a systemic and tissue-specific level.

Skin Metabolomics

Skin retains numerous low-molecular-weight compounds (metabolites). Some of these compounds fulfill specific physiological roles, while others are by-products of metabolism. The skin surface can be sampled to detect and quantify skin metabolites related to diseases. Miniature probes have been developed to detect selected high-abundance metabolites secreted with sweat. To characterize a broad spectrum of skin metabolites, specimens are collected with one of several available methods, and the processed specimens are analyzed by chromatography, mass spectrometry (MS), or other techniques. Diseases for which skin-related biomarkers have been found include cystic fibrosis (CF), psoriasis, Parkinson's disease (PD), and lung cancer. To increase the clinical significance of skin metabolomics, it is desirable to verify correlations between metabolite levels in skin and other biological tissues/matrices.

STRATEGIES AND CHALLENGES IN METHOD DEVELOPMENT AND VALIDATION FOR THE ABSOLUTE QUANTIFICATION OF ENDOGENOUS BIOMARKER METABOLITES USING LIQUID CHROMATOGRAPHY-TANDEM MASS SPECTROMETRY

Metabolomics is a dynamically evolving field, with a major application in identifying biomarkers for drug development and personalized medicine. Numerous metabolomic studies have identified endogenous metabolites that, in principle, are eligible for translation to clinical practice. However, few metabolomic-derived biomarker candidates have been qualified by regulatory bodies for clinical applications. Such interruption in the biomarker qualification process can be largely attributed to various reasons including inappropriate study design and inadequate data to support the clinical utility of the biomarkers. In addition, the lack of robust assays for the routine quantification of candidate biomarkers has been suggested as a potential bottleneck in the biomarker qualification process. In fact, the nature of the endogenous metabolites precludes the application of the current validation guidelines for bioanalytical methods. As a result, there have been individual efforts in modifying existing guidelines and/or developing alternative approaches to facilitate method validation. In this review, three main challenges for method development and validation for endogenous metabolites are discussed, namely matrix effects evaluation, alternative analyte-free matrices, and the choice of internal standards (ISs). Some studies have modified the equations described by the European Medicines Agency for the evaluation of matrix effects. However, alternative strategies were also described; for instance, calibration curves can be generated in solvents and in biological samples and the slopes can be compared through ratios, relative standard deviation, or a modified Stufour suggested approaches while quantifying mainly endogenous metabolitesdent t-test. ISs, on the contrary, are diverse; in which seven different possible types, used in metabolomics-based studies, were identified in the literature. Each type has its advantages and limitations; however, isotope-labeled ISs and ISs created through isotope derivatization show superior performance. Finally, alternative matrices have been described and tested during method development and validation for the quantification of endogenous entities. These alternatives are discussed in detail, highlighting their advantages and shortcomings. The goal of this review is to compare, apprise, and debate current knowledge and practices in order to aid researchers and clinical scientists in developing robust assays needed during the qualification process of candidate metabolite biomarkers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

Metabolomics in postmortem cerebrospinal fluid diagnostics: a state-of-the-art method to interpret central nervous system-related pathological processes

In the last few years, quantitative analysis of metabolites in body fluids using LC/MS has become an established method in laboratory medicine and toxicology. By preparing metabolite profiles in biological specimens, we are able to understand pathophysiological mechanisms at the biochemical and thus the functional level. An innovative investigative method, which has not yet been used widely in the forensic context, is to use the clinical application of metabolomics. In a metabolomic analysis of 41 samples of postmortem cerebrospinal fluid (CSF) samples divided into cohorts of four different causes of death, namely, cardiovascular fatalities, isoIated torso trauma, traumatic brain injury, and multi-organ failure, we were able to identify relevant differences in the metabolite profile between these individual groups. According to this preliminary assessment, we assume that information on biochemical processes is not gained by differences in the concentration of individual metabolites in CSF, but by a combination of differently distributed metabolites forming the perspective of a new generation of biomarkers for diagnosing (fatal) TBI and associated neuropathological changes in the CNS using CSF samples.

A Serum Metabolic Profiling Analysis During the Formation of Fatty Liver in Landes Geese via GC-TOF/MS

During the process of fatty liver production by overfeeding, the levels of endogenous metabolites in the serum of geese would change dramatically. This study investigated the effects of overfeeding on serum metabolism of Landes geese and the underlying mechanisms using a metabolomics approach. Sixty Landes geese of the same age were randomly divided into the following three groups with 20 replicates in each group: D0 group (free from gavage); D7 group (overfeeding for 7 days); D25 group (overfeeding for 25 days). At the end of the experiment, 10 geese of similar weight from each group were selected for slaughter and sampling. The results showed that overfeeding significantly increased the body weight and the liver weight of geese. Serum enzymatic activities and serum lipid levels were significantly enhanced following overfeeding. Gas chromatography time-of-flight/mass spectrometry (GC-TOF/MS) was employed to explore the serum metabolic patterns, and to identify potential contributors to the formation of fatty liver and the correlated metabolic pathways. Relative to overfeeding for 7 days, a large number of endogenous molecules in serum of geese overfed for 25 days were altered. Continuous elevated levels of pyruvic acid, alanine, proline and beta-glycerophosphoric acid and reduced lactic acid level were observed in the serum of overfed geese. Pathway exploration found that the most of significantly different metabolites were involved in amino acids, carbohydrate and lipid metabolism. The present study exhibited the efficient capability of Landes geese to produce fatty liver, identified potential biomarkers and disturbed metabolic pathways in liver steatosis. These findings might reveal the underlying mechanisms of fatty liver formation and provide some theoretical basis for the diagnosis and treatment of liver diseases.

Effect of punicalagin on multiple targets in streptozotocin/high-fat diet-induced diabetic mice

Type 2 diabetes has a series of metabolic aberrations accompanied by chronic hyperglycemia, along with various comorbidities. In recent reports, punicalagin from pomegranate has been reported to exert hypoglycemic effects against diabetes. The goal of the current research was to investigate the therapeutic effectiveness and elucidate the mechanisms of punicalagin underlying type 2 diabetes. Type 2 diabetes was induced by a high-fat diet (HFD) combined with streptozotocin (STZ) injection in C57BL/6J mice. Punicalagin was administered daily by oral gavage for 4 weeks. The results indicated that high FBG (fasting blood glucose), dyslipidemia and associated islet, liver and kidney injury were observed in the model group mice. Through metabolomics analysis, it was found that the administration of punicalagin could regulate 24 potential biomarkers and their related metabolic pathways. Moreover, the pathological changes in the liver and kidney were mainly mediated by reducing gluconeogenesis and increasing glycogenesis via stimulation of the PI3K/AKT signaling pathway and regulation of the HMGB-1/TLR4/NF-κB signaling pathway, which simultaneously interrelated to ten main pathological pathways. In addition, we confirmed the positive role of punicalagin in glucosamine-induced HepG2 cells and HG-induced HK-2 cells through related mechanistic studies in vitro. In conclusion, these findings suggested that the multi-effect and multi-target action mode of punicalagin had a significant hypoglycemic effect and a protective effect on diabetes mellitus. Punicalagin might serve as an alternative functional food or as a clinical supplemental therapy for the diabetic population to ameliorate metabolic syndrome.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

The specific metabolome profiling of patients infected by SARS-COV-2 supports the key role of tryptophan-nicotinamide pathway and cytosine metabolism

The biological mechanisms involved in SARS-CoV-2 infection are only partially understood. Thus we explored the plasma metabolome of patients infected with SARS-CoV-2 to search for diagnostic and/or prognostic biomarkers and to improve the knowledge of metabolic disturbance in this infection. We analyzed the plasma metabolome of 55 patients infected with SARS-CoV-2 and 45 controls by LC-HRMS at the time of viral diagnosis (D0). We first evaluated the ability to predict the diagnosis from the metabotype at D0 in an independent population. Next, we assessed the feasibility of predicting the disease evolution at the 7th and 15th day. Plasma metabolome allowed us to generate a discriminant multivariate model to predict the diagnosis of SARS-CoV-2 in an independent population (accuracy > 74%, sensitivity, specificity > 75%). We identified the role of the cytosine and tryptophan-nicotinamide pathways in this discrimination. However, metabolomic exploration modestly explained the disease evolution. Here, we present the first metabolomic study in SARS-CoV-2 patients which showed a high reliable prediction of early diagnosis. We have highlighted the role of the tryptophan-nicotinamide pathway clearly linked to inflammatory signals and microbiota, and the involvement of cytosine, previously described as a coordinator of cell metabolism in SARS-CoV-2. These findings could open new therapeutic perspectives as indirect targets.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach

BACKGROUND

To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC).

METHODS

A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis.

RESULTS

Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3'4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity.

CONCLUSION

The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology

Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.

Serum Metabolomics Revealed the Differential Metabolic Pathway in Calves with Severe Clinical Diarrhea Symptoms

Simple Summary

The present study focuses on the metabolic changes in the diarrhea of calves, which are manifested with the following symptoms: a thin water-like stool, cold ears and nose, throbbing bowels, oliguria, a pale or yellowish complexion, a smooth mouth, and a slow pulse. The differential metabolic pathways in calves with diarrhea were screened by metabolomics. There were nine biomarkers in the serum of healthy calves and calves with diarrhea. On the basis of these biomarkers, their associated mineral absorption, protein digestion and absorption, and other metabolic pathways, the targeted regulation of the metabolic differences of calves with diarrhea may contribute to the diagnosis, treatment, and discussion of the mechanism of calf diarrhea.

Abstract

The complex etiology, higher morbidity and mortality, poor prognosis, and expensive cost of calf diarrhea have made it a catastrophic disease in the dairy industry. This study aims to assess the biomarkers in calves with diarrhea and to predict the biomarkers related to the pathway. As subjects, nine calves with diarrhea and nine healthy calves were enrolled, according to strict enrollment criteria. The serum metabolites were detected by a liquid chromatographic tandem mass spectrometry (LC-MS/MS), and then analyzed by online multivariate statistical analysis software to further screen the biomarkers. In addition, the biomarkers involved in the metabolic pathways of calves with diarrhea and healthy calves were analyzed. In the serum of calves with diarrhea, nine biomarkers were found to which several biomarkers exhibited a certain relation. Moreover, these biomarkers were involved in important metabolic pathways, including protein digestion and absorption, ABC transporters, aminoacyl-tRNA biosynthesis, mineral absorption, and fatty acid biosynthesis. All these findings suggested that the imbalance of these markers was closely related to the occurrence and development of calf diarrhea. The targeted regulation of metabolic pathways involved in these biomarkers may facilitate the diagnosis, treatment, and discussion of the mechanism of calf diarrhea.

A microbiome and metabolomic signature of phases of cutaneous healing identified by profiling sequential acute wounds of human skin: An exploratory study

Profiling skin microbiome and metabolome has been utilised to gain further insight into wound healing processes. The aims of this multi-part temporal study in 11 volunteers were to analytically profile the dynamic wound tissue and headspace metabolome and sequence microbial communities in acute wound healing at days 0, 7, 14, 21 and 28, and to investigate their relationship to wound healing, using non-invasive quantitative devices. Metabolites were obtained using tissue extraction, sorbent and polydimethylsiloxane patches and analysed using GCMS. PCA of wound tissue metabolome clearly separated time points with 10 metabolites of 346 being involved in separation. Analysis of variance-simultaneous component analysis identified a statistical difference between the wound headspace metabolome, sites (P = 0.0024) and time points (P<0.0001), with 10 out of the 129 metabolites measured involved with this separation between sites and time points. A reciprocal relationship between Staphylococcus spp. and Propionibacterium spp. was observed at day 21 (P<0.05) with a statistical correlation between collagen and Propionibacterium (r = 0.417; P = 0.038) and Staphylococcus (r = -0.434; P = 0.03). Procrustes analysis showed a statistically significant similarity between wound headspace and tissue metabolome with non-invasive wound devices. This exploratory study demonstrates the temporal and dynamic nature of acute wound metabolome and microbiome presenting a novel class of biomarkers that correspond to wound healing, with further confirmatory studies now necessary.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.